ChatGPT and DeepSeek-R1 in the Standardized Training of Radiation Oncology Residents: Potential Applications and Limitations (Preprint)

Abstract

<sec> <title>UNSTRUCTURED</title> With the rapid development of artificial intelligence (AI), particularly the emergence of generative AI technologies such as ChatGPT and DeepSeek-R1, their potential applications in medical education are becoming increasingly evident. This paper explores the role of ChatGPT in standardized training for radiation oncology residents, examining its advantages and limitations while providing a brief comparison with the emerging AI model, DeepSeek-R1. ChatGPT serves as an auxiliary tool for licensing exam preparation, real-time knowledge retrieval, virtual clinical training, and research support. However, its application in medical education presents both opportunities and challenges, such as potential “hallucinations,” data privacy concerns, and the risk of learning dependency which necessitate careful supervision and critical evaluation. Radiation oncology residents must develop critical thinking skills to effectively utilize AI-generated content. ChatGPT and DeepSeek-R1 offer different advantages in radiation oncology training. DeepSeek-R1 excels in real-time knowledge retrieval, medical image analysis, and computational tasks, making it particularly suitable for data-driven applications such as dose calculation and imaging interpretation. In contrast, ChatGPT, with its strong theoretical foundation and natural language processing capabilities, is well-suited for theoretical education, case discussions, and communication training. This suggests that integrating different AI models, such as ChatGPT and DeepSeek-R1, can create a more balanced and effective learning system. The synergy between AI and medical education may drive transformative advancements, ultimately optimizing training programs and improving clinical competency in radiation oncology. </sec>

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